aszerW
ee2453f65e
- 问题：cum_return_idx 和 cum_return_etf 使用相同的 ETF 价格计算 - 修复：分别使用指数价格（raw）和 ETF 价格（hfq）独立计算 - 验证：72.6% 的持仓记录显示差异（0.06%~0.48%），符合预期 - 新增验证脚本：verify_cum_return_fix.py
337 lines
12 KiB
Python
337 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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测算 ETF 跳空收益（Gap Return）对策略的影响
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测算目标：
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1. 量化各 ETF 的跳空特征（幅度、频率、波动率）
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2. 分析跳空对策略收益的实际影响
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3. 判断是否需要修改收益计算逻辑
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用法:
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python framework_v2/scripts/measure_gap_impact.py
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"""
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import sys
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from pathlib import Path
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import numpy as np
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import pandas as pd
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project_root = Path(__file__).parent.parent.parent
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sys.path.insert(0, str(project_root))
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from dotenv import load_dotenv
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load_dotenv()
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from framework_v2.config import load_config
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from framework_v2.strategies.rotation.rotation import GlobalRotationStrategy
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from framework_v2.shared.data import FlaskAPIFetcher
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def fetch_etf_data_with_ohlc(codes, start, end):
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"""获取 ETF 的 OHLC 数据（hfq）"""
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fetcher = FlaskAPIFetcher()
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print(f"\n[数据获取] 获取 {len(codes)} 只 ETF 的 OHLC 数据（hfq）...")
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data = {}
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for i, code in enumerate(codes, 1):
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print(f" [{i}/{len(codes)}] {code}...")
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df = fetcher._source.fetch(
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code=code,
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start_date=start,
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end_date=end,
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adj='hfq',
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asset_type='china_etf'
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)
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if df is not None:
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data[code] = df
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print(f" ✓ {len(df)} 条")
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else:
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print(f" ✗ 获取失败")
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return data
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def calculate_gap_statistics(etf_data):
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"""计算各 ETF 的跳空统计"""
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print("\n" + "=" * 80)
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print(" 跳空收益统计分析")
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print("=" * 80)
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stats_list = []
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for code, df in etf_data.items():
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# 确保按日期排序
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df = df.sort_index()
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# 计算收益率
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prev_close = df['close'].shift(1)
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# 跳空收益率：(T_open - T-1_close) / T-1_close
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gap_return = (df['open'] - prev_close) / prev_close
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# 日内收益率：(T_close - T_open) / T_open
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intraday_return = (df['close'] - df['open']) / df['open']
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# 验证：总收益率 ≈ 跳空 + 日内
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total_return = df['close'].pct_change()
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# 统计指标
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stats = {
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'ETF': code,
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'数据天数': len(df),
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'平均跳空(%)': gap_return.mean() * 100,
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'跳空波动率(%)': gap_return.std() * 100,
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'向上跳空比例(%)': (gap_return > 0.0001).sum() / len(gap_return) * 100,
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'向下跳空比例(%)': (gap_return < -0.0001).sum() / len(gap_return) * 100,
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'最大向上跳空(%)': gap_return.max() * 100,
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'最大向下跳空(%)': gap_return.min() * 100,
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'平均日内收益(%)': intraday_return.mean() * 100,
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'日内波动率(%)': intraday_return.std() * 100,
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'跳空>1%天数': (gap_return.abs() > 0.01).sum(),
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'跳空>2%天数': (gap_return.abs() > 0.02).sum(),
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}
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stats_list.append(stats)
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# 转换为 DataFrame
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stats_df = pd.DataFrame(stats_list)
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# 打印统计表格
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print("\n各 ETF 跳空收益统计：")
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print("-" * 80)
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for _, row in stats_df.iterrows():
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print(f"\n{row['ETF']}:")
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print(f" 数据天数: {row['数据天数']}")
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print(f" 平均跳空: {row['平均跳空(%)']:+.3f}% (波动率: {row['跳空波动率(%)']:.2f}%)")
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print(f" 向上跳空: {row['向上跳空比例(%)']:.1f}% 向下: {row['向下跳空比例(%)']:.1f}%")
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print(f" 最大跳空: +{row['最大向上跳空(%)']:.2f}% / {row['最大向下跳空(%)']:.2f}%")
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print(f" 跳空>1%: {row['跳空>1%天数']}天 >2%: {row['跳空>2%天数']}天")
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print(f" 平均日内收益: {row['平均日内收益(%)']:+.3f}%")
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return stats_df
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def analyze_strategy_gap_impact(strategy, etf_data):
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"""分析跳空对策略的实际影响"""
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print("\n" + "=" * 80)
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print(" 策略跳空影响分析")
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print("=" * 80)
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# 1. 获取策略持仓数据
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print("\n[1] 获取策略持仓数据...")
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# 运行策略获取信号和仓位
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from datetime import date
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config = strategy.config
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start = config.backtest.start_date
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end = config.backtest.end_date
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if end is None:
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end = date.today().strftime('%Y-%m-%d')
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# 运行策略（不导出 JSON）
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result = strategy.run(export_detail=False)
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positions = result['positions']
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trading_calendar = positions.index
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# 2. 计算新旧两种收益
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print("\n[2] 计算两种收益方法...")
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signal_to_trade = config.asset_pools.get_signal_to_trade_mapping()
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# 准备数据
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close_dict = {}
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open_dict = {}
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for signal_code, trade_code in signal_to_trade.items():
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if trade_code in etf_data:
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df = etf_data[trade_code]
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# 对齐到 A 股日历
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close_dict[signal_code] = df['close'].reindex(trading_calendar, method='ffill')
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open_dict[signal_code] = df['open'].reindex(trading_calendar, method='ffill')
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close_df = pd.DataFrame(close_dict)
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open_df = pd.DataFrame(open_dict)
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# 方法 1：旧方法（close-to-close）
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positions_delayed = positions.shift(1).fillna(0)
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old_returns_df = close_df.pct_change()
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old_strategy_returns = (positions_delayed * old_returns_df).sum(axis=1)
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# 方法 2：新方法（分段计算）
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prev_positions = positions_delayed.shift(1).fillna(0)
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curr_positions = positions_delayed
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# 检测状态
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is_buying = (prev_positions == 0) & (curr_positions > 0)
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is_holding = (prev_positions > 0) & (curr_positions > 0)
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is_selling = (prev_positions > 0) & (curr_positions == 0)
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# 计算各类收益率
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buy_returns = (close_df - open_df) / open_df # open-to-close
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hold_returns = close_df.pct_change() # close-to-close
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sell_returns = (open_df - close_df.shift(1)) / close_df.shift(1) # close-to-open
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# 组合收益率
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new_returns_df = pd.DataFrame(0.0, index=close_df.index, columns=close_df.columns)
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new_returns_df[is_buying] = buy_returns[is_buying]
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new_returns_df[is_holding] = hold_returns[is_holding]
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new_returns_df[is_selling] = sell_returns[is_selling]
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new_strategy_returns = (curr_positions * new_returns_df).sum(axis=1)
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# 3. 计算净值曲线和 KPI
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print("\n[3] 计算净值曲线和 KPI 对比...")
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old_equity = (1 + old_strategy_returns).cumprod()
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new_equity = (1 + new_strategy_returns).cumprod()
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def calc_kpi(returns, equity, name):
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total_return = equity.iloc[-1] / equity.iloc[0] - 1
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n_days = len(returns)
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annual_return = (1 + total_return) ** (252 / n_days) - 1
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cummax = equity.cummax()
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drawdown = (equity - cummax) / cummax
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max_drawdown = drawdown.min()
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sharpe = returns.mean() / returns.std() * np.sqrt(252) if returns.std() > 0 else 0
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print(f"\n {name}:")
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print(f" 总收益: {total_return * 100:.2f}%")
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print(f" 年化收益: {annual_return * 100:.2f}%")
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print(f" 最大回撤: {max_drawdown * 100:.2f}%")
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print(f" 夏普比率: {sharpe:.2f}")
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print(f" 交易天数: {n_days}")
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return {
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'总收益': total_return,
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'年化收益': annual_return,
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'最大回撤': max_drawdown,
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'夏普比率': sharpe,
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}
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old_kpi = calc_kpi(old_strategy_returns, old_equity, "旧方法（close-to-close）")
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new_kpi = calc_kpi(new_strategy_returns, new_equity, "新方法（分段计算）")
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# 4. 差异分析
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print("\n" + "=" * 80)
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print(" 差异对比")
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print("=" * 80)
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print(f"\n {'指标':<12} {'旧方法':>12} {'新方法':>12} {'差异':>12}")
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print(f" {'-'*12} {'-'*12} {'-'*12} {'-'*12}")
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for key in ['总收益', '年化收益', '最大回撤', '夏普比率']:
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old_val = old_kpi[key]
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new_val = new_kpi[key]
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diff = new_val - old_val
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if key == '夏普比率':
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print(f" {key:<12} {old_val:>12.2f} {new_val:>12.2f} {diff:>+12.2f}")
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else:
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print(f" {key:<12} {old_val*100:>11.2f}% {new_val*100:>11.2f}% {diff*100:>+11.2f}%")
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# 5. 调仓日分析
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print("\n" + "=" * 80)
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print(" 调仓日跳空分析")
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print("=" * 80)
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# 识别调仓日
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position_changes = (positions != positions.shift(1)).any(axis=1)
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rebalance_dates = positions[position_changes].index
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print(f"\n 总调仓次数: {len(rebalance_dates)}")
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# 分析调仓日的跳空
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gap_returns_all = []
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for date in rebalance_dates:
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if date in close_df.index:
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# 计算该日的平均跳空（所有持仓 ETF）
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pos = positions.loc[date]
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held_codes = pos[pos > 0].index
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if len(held_codes) > 0:
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# 过滤掉不在 open_df 中的代码（如指数）
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held_codes = [c for c in held_codes if c in open_df.columns]
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if len(held_codes) == 0:
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continue
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day_gap = open_df.loc[date][held_codes]
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prev_close = close_df.shift(1).loc[date][held_codes]
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gap = (day_gap - prev_close) / prev_close
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gap_returns_all.append(gap.mean())
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if gap_returns_all:
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gap_series = pd.Series(gap_returns_all)
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print(f"\n 调仓日跳空统计：")
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print(f" 平均跳空: {gap_series.mean() * 100:+.3f}%")
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print(f" 跳空标准差: {gap_series.std() * 100:.2f}%")
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print(f" 最大向上跳空: {gap_series.max() * 100:+.2f}%")
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print(f" 最大向下跳空: {gap_series.min() * 100:+.2f}%")
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print(f" 向上跳空天数: {(gap_series > 0).sum()} ({(gap_series > 0).sum() / len(gap_series) * 100:.1f}%)")
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print(f" 向下跳空天数: {(gap_series < 0).sum()} ({(gap_series < 0).sum() / len(gap_series) * 100:.1f}%)")
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else:
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print(f"\n ⚠ 无法计算调仓日跳空（数据缺失）")
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return old_kpi, new_kpi
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def main():
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print("=" * 80)
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print(" ETF 跳空收益影响测算")
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print("=" * 80)
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# 1. 加载配置
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config_file = project_root / 'framework_v2' / 'strategies' / 'rotation' / 'config_simple.yaml'
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print(f"\n[1] 加载配置: {config_file}")
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config = load_config(str(config_file))
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# 2. 获取 ETF 列表
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signal_to_trade = config.asset_pools.get_signal_to_trade_mapping()
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trade_codes = list(set(signal_to_trade.values()))
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# 过滤掉不是 ETF 的代码（如 931862.CSI）
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trade_codes = [c for c in trade_codes if not c.endswith('.CSI')]
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print(f" ETF 数量: {len(trade_codes)}")
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# 3. 获取数据
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from datetime import date
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start = config.backtest.start_date
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end = config.backtest.end_date
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if end is None:
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end = date.today().strftime('%Y-%m-%d')
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etf_data = fetch_etf_data_with_ohlc(trade_codes, start, end)
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# 4. 计算跳空统计
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stats_df = calculate_gap_statistics(etf_data)
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# 5. 分析策略影响
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strategy = GlobalRotationStrategy(config)
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old_kpi, new_kpi = analyze_strategy_gap_impact(strategy, etf_data)
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# 6. 结论
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print("\n" + "=" * 80)
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print(" 结论与建议")
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print("=" * 80)
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annual_diff = new_kpi['年化收益'] - old_kpi['年化收益']
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if abs(annual_diff) < 0.01: # 差异 < 1%
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print("\n ✓ 跳空影响较小（< 1%），可以继续使用 close-to-close 简化计算")
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elif abs(annual_diff) < 0.03: # 差异 1-3%
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print("\n ⚠ 跳空影响中等（1-3%），建议考虑使用分段计算提高精度")
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else: # 差异 > 3%
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print("\n ✗ 跳空影响显著（> 3%），强烈建议使用分段计算")
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print(f"\n 当前年化: {old_kpi['年化收益'] * 100:.2f}%")
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print(f" 修正后年化: {new_kpi['年化收益'] * 100:.2f}%")
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print(f" 差异: {annual_diff * 100:+.2f}%")
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print("=" * 80)
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if __name__ == '__main__':
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main()
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